Data-driven software reliability evaluation under incomplete knowledge on fault count distribution. Issue Volume 32:Issues 3(2020) (2nd July 2020)

• Record Type: Journal Article
• Authors: Dohi, Tadashi; Zheng, Junjun; Okamura, Hiroyuki
• Is Part Of: Quality engineering. Volume 32:Issues 3(2020)
• Page Start: 421
• Page End: 433
• Publication Date: 2020-07-02
• Subjects: Bootstrap -- confidence region -- data-driven approach -- kernel-based method -- non-homogeneous Poisson processes -- non-parametric estimation -- software reliability; Quality control -- Periodicals; Production management -- Quality control -- Periodicals; 658.5
• DOI: 10.1080/08982112.2020.1757705
• Languages: English
• ISSNs: 0898-2112

Abstract

Abstract: In this article, we consider data-driven approaches for software reliability evaluation without specifying the fault count distribution, where the underlying stochastic process to describe software fault-counts in the system testing is given by a non-homogeneous Poisson process. A comprehensive non-parametric method based on the kernel estimation is provided with several kernel functions and bandwidth estimations, in addition to the non-parametric bootstrap. The resulting data-driven methodologies can give useful probabilistic information on the software reliability prediction under the incomplete knowledge on fault count distribution.